UNCOVERING A MESSAGE FROM OUR PAST

Excavations at the cave of Santa Ana Photo Creative Commons Mario Modesto

​by Gregory Saville

I came across an interesting artifact while digging through some old conference files this week. It brought to mind a story…

He was already a legend when he attended A Crime Prevention Workshop, the 1975 Toronto event that introduced CPTED to Canada. He had been a one-time cat-burglary (until his arrest and prison term) and he was a former Hollywood stunt man on the original Mutiny on the Bounty. University of Alberta’s Professor Gwynn Nettler delivered what must have been the most provocative paper at the event – he questioned how it was possible to do crime prevention with such poor quality science within the social sciences – a topic that would later come back to haunt today’s social sciences. 

By all measures, Professor Nettler, a Stanford trained sociologist and arguably Canada’s most pre-eminent criminologist, was a charismatic, academic iconoclast. Time magazine once called him “a wonderful burglar” and later, the American Society of Criminology awarded him its highest award.

Nettler's classic text: Boundaries of Competence: How Social Studies Make Feeble Science

In his eulogy, the American Sociological Association wrote he “cherished music, from opera to Ellington, and cut a dashing figure with his presence, the sports cars he drove, and especially the ladies he loved.” He was the Indiana Jones of the criminology world - at least in my quarantine-deprived imagination. 


SEARCHING FOR THE HOLY GRAIL

Nettler often wrote about the impotence of science within social research. “The first part of becoming a scientist…is to be able to recognize rules that merely draw circles and rules that are so phrased that everything that happens confirms them and nothing that happens disconfirms them.” As we outlined a few blogs ago, this is the essential flaw in the Routine Activity Theory of crime.

Arthur Hughes 1870 painting "Sir Galahad, the Quest for the Holy Grail" - Photo Creative Commons

​Theoretically trained scientists are taught how to recognize the error of logic circles – which is why the holy trinity of a motivated offender/capable guardian/suitable target cannot predict anything with accuracy. And without prediction, Nettler reminds us, it is neither science nor a theory. If we are to move forward in crime prevention, we must have legitimate theories on which to base our work!

For example, it might be attractive to surmise that poor lighting avails criminals to commit strongarm robberies at night when unsuspecting victims walk by. It might – if it were true. But if turning on more lights makes it easier for the crook to locate his victim, (in other words, the motivated offender adapts to the suitability of his target) then lighting provides neither the answer nor the prevention. 

Or worse, as suggested recently in the Black Lives Matter movement, if CPTED controls access into some urban areas in the hope that excluding “outsiders” will prevent crime, it may end up targeting some races and income groups from others. Exclusionary crime prevention theories will not make things better, as we see in today’s racial protests.  

Exclusionary crime prevention? Even the Great Wall of China didn't work. Photo Jakub Halun

​CRIME PREVENTION MUST DO BETTER

While Nettler described some neighborhoods as triggers for crime, he was sceptical of standalone causes, like poverty and ghetto housing. The Indiana Jones of the 1970s knew enough of science to insist on clarity and reliable observations to support concepts. So what does such a theory look like within the crime prevention world today?

I have insisted at each step in our SafeGrowth work that we seize on a well-established concept of social cohesion called neighborhood collective efficacy. 

Consider for a moment a neighborhood suffering from poverty, inequity, poor relations among residents, dilapidated infrastructure and housing, and hopelessness. It will be of little surprise that in such places you find yourself facing high crime and victimization risks. Incidents of street violence and fear will outstrip other areas in the city, and demands for social services will produce an exorbitant strain on municipal coffers. Locations like this are not the only place of crime, nor do they house all kinds of crime. But there is little doubt crime concentrates in such places. 

Crime concentrates in specific city neighborhoods

​Walk a short distance and you will discover neighborhoods with average incomes, adequate basic services, friendliness among neighbors, functioning infrastructure and decent housing, and some degree of happiness. Here you will uncover a fairly safe place with low crime and victimization. Incidents of street violence will be rare and municipal service providers will rarely visit such places. 

Nettler described such places in his classic text Explaining Crime. Research from the geography of crime shows these patterns all over the world; crime hotspots cluster in the first type of neighborhood, not in the second. The consistency of these observations provides a sound basis to build a crime prevention theory. 

So, we have. 


HOW?

In SafeGrowth we put this theory into practice through community development and social cohesion. Our work predicts and produces safer and more livable neighborhoods. This occurs from Christchurch, New Zealand and Toronto, to Hollygrove in New Orleans, New York, and in  Philadelphia.

This is a robust theory of crime prevention that comes close to what Nettler described in his search for the building blocks of a good theory. Few social theories of any kind approach the precision of the general theory of relativity in physics. But collective efficacy and SafeGrowth are among those that aim for that quality.  

When the pandemic threats and racial unrest that plague our streets begin to subside, we will search for ways out of our collective mess. Like the archaeologist seeking answers to long lost questions, we will need answers about how to rebuild safe and livable neighborhoods.

We need not look very far.